2014/08/28 by Marcello Farina, Luca Giulioni, Farina, Marcello +5
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Stability and Control of Uncertain Systems #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1408.6723
openalex publication_date 2014/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we propose an output-feedback Model Predictive Control (MPC)\nalgorithm for linear discrete-time systems affected by a possibly unbounded\nadditive noise and subject to probabilistic constraints. In case the noise\ndistribution is unknown, the chance constraints on the input and state\nvariables are reformulated by means of the Chebyshev - Cantelli inequality. The\nrecursive feasibility of the proposed algorithm is guaranteed and the\nconvergence of the state to a suitable neighbor of the origin is proved under\nmild assumptions. The implementation issues are thoroughly addressed showing\nthat, with a proper choice of the design parameters, its computational load can\nbe made similar to the one of a standard stabilizing MPC algorithm. Two\nexamples are discussed in details, with the aim of providing an insight on the\nperformance achievable by the proposed control scheme.\n